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Data management is fundamental to every application. Managing this precious asset is an essential competency in modern businesses of every sort. Innovations in data platforms are being adopted, and data management approaches are evolving rapidly to keep pace. Increasingly, enterprises are converging their data warehouse, data lake, and other data management platforms onto distributed cloud-native infrastructures. As more types of data are consolidated into their platforms, enterprises implement more scalable DataOps pipelines and more comprehensive governance practices to manage it all.

As organizations seek to become more competitive, they are often looking to enrich their data sets for analytics to gain deeper insights. The data used for enrichment may include text data, machine data, image data, geospatial data, and real-time data. This data may be high volume, highly diverse, and disparate in nature. As part of this effort, organizations are moving to cloud data platforms to store and manage this modern data.

Designed to be fast, scalable, flexible, and user-friendly, modern applications are at the center of the innovation and automation that is transforming companies, industries, and society today. At the same time, modern applications, increasingly built with microservices, also come with requirements that traditional data management approaches fall far short of effectively meeting.

Data teams need to be able to set cloud data budgets at a specific scope - and know if your various teams or departments are tracking to those budgets. But today, most data teams only know that the budget was overrun after it’s too late. With Unravel, establishing and tracking budgets to prevent overruns is easy.

Bringing Your Data Cloud Bill Under Control presented by Keith Alsheimer, CMO, Unravel Data at CDAO Fall 2022. During this presentation, Keith shares how the Unravel Data DataOps observability platform helps data teams.

From data lakes and data warehouses to data mesh and data fabric architectures, the world of analytics continues to evolve to meet the demand for fast, easy, wide-ranging data insights. Right now, nearly 50% of DBTA subscribers are using public cloud services, and many are investing further in staff, skills, and solutions to address key technical challenges. Even today, the amount of time and resources most organizations spend analyzing data pales in comparison to the effort expended in identifying, cleansing, rationalizing, consolidating, and transforming that data.

We have closed a $50 million Series D round of funding, led by Third Point Ventures with participation from Bridge Bank and existing investors that include Menlo Ventures, Point72 Ventures, GCV Capital, and Harmony Capital. Kunal Agarwal, CEO and Co-Founder, shares what this funding means for you.

Unravel is a DataOps observability platform that enables your data teams to optimize the cost of your data operations intelligently, run faster data pipelines, and troubleshoot mission-critical applications. Watch this video to discover what’s new in Unravel Data 4.7.5.0 release.

For DataOps teams, job failures are common. But finding the issue is (traditionally) where things get even worse. It can take hours or days to troubleshoot a job failure. Unravel Data provides a single view where DataOps teams can locate exactly where–and why–a job failed, along with precise recommendations to troubleshoot the error. DataOps teams are now able to both diagnose and troubleshoot job failures in minutes instead of days or weeks.

Data pipelines fail all the time for a variety of reasons; service downtime, data volume fluctuations, etc. Diagnosing these failures manually is very difficult and time consuming. Unravel Data allows DataOps teams to troubleshoot pipeline failures automatically – showing exactly where and why a pipeline failed, and precise recommendations to remedy the issues. Using Unravel, DataOps teams can now diagnose and fix data pipeline failures in a fraction of the time.